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1.What are the top/worst selling products in terms of sales?
a.Is there a difference in payments methods?
2.Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?
3.Is there a difference in sales and gross profits between different months of the year?
4.Are their differences in sales performance between different seasons?
a.How does this relate to rainfall and profits?

Results and discussion

Businesses need an understanding of the progress of the business throughout the year. It should be able to determine whether their sales/profit and cost of goods sold is different in different seasons and the month of the year. The main challenge of businesses is determining whether their net sales, profit among other crucial factors are affected by rainfall. Therefore, this research will use different business analytic approaches to gain insight into the business performance. The research is designed to understand the business performance. Each subsection will answer a specific research question.

 Statistics Total Sales (\$) Cost of Goods (\$) Net Profit (\$) N Valid 1034 1034 1034 Missing 0 0 0 Mean 369.96 205.22 164.74 Median 85.15 49.55 35.02 Std. Deviation 1014.719 561.072 482.106 Skewness 8.511 8.325 9.234 Std. Error of Skewness .076 .076 .076 Percentiles 25 32.62 16.10 13.65 75 280.76 162.19 110.46

The average sales total is \$369.96 (SD = \$1014.72). The median of the total sales is significantly lower than the average, which implies that the data are very skewed. The skewness coefficient is 8.511 indicating a case of very skewed data. Notably, all the three variables (Total Sales (\$), Cost of Goods (\$), and Net Profit (\$)) are very skewed (Skew Coef. > 3.0). The middle 50% of the total sales is between \$32.62 and \$280.76. The average cost of goods sold is \$205.22 (SD = \$561.07). The middle 50% of the cost of goods sold is between \$16.10 and \$162.19. The business earns on average \$164.74 (SD =\$482.106) net profit. The middle 50% of the net profit lies between \$13.65 and \$110.46. The most significant issue that can be obtained from this is that the average total sales are higher than the cost of goods sold which indicate that the business is earning a profit.

An assessment was carried out to determine whether the different method of payment adopted rate of performance is the same. This was aimed at determining whether there was a difference in the method of payment. A paired t-test was carried out for different payment methods, and the results are as follows.

 Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Cash_Total - Credit_Total -180.512 236.235 12.348 -204.794 -156.229 -14.618 365 .000 Pair 2 Cash_Total - Visa_Total -151.563 249.173 13.024 -177.175 -125.950 -11.637 365 .000 Pair 3 Cash_Total - Mastercard_Total 382.192 170.933 8.935 364.622 399.763 42.776 365 .000 Pair 4 Cash_Total - House_Account 366.899 178.242 9.317 348.577 385.220 39.380 365 .000 Pair 5 Credit_Total - Visa_Total 28.949 89.437 4.675 19.756 38.142 6.192 365 .000 Pair 6 Credit_Total - Mastercard_Total 562.704 236.975 12.387 538.346 587.063 45.427 365 .000 Pair 7 Credit_Total - House_Account 547.410 248.710 13.000 521.846 572.975 42.108 365 .000 Pair 8 Visa_Total - Mastercard_Total 533.755 274.257 14.336 505.564 561.946 37.233 365 .000 Pair 9 Visa_Total - House_Account 518.462 261.812 13.685 491.550 545.373 37.885 365 .000 Pair 10 Mastercard_Total - House_Account -15.294 133.405 6.973 -29.006 -1.581 -2.193 365 .029

The t-values (365), p < .05 shows that adequate evidence exists to reject the null hypothesis (Rietveld & Hout, 2017). In summary, the findings indicate that all the method of payment is performing significantly different.

An assessment to determine which product has the highest sale, and the least sales were performed. For accuracy, the profit yield by each product should be used. The analysis results indicate that water has the highest average total profit of \$884.29 (SD = 1188.119). The second best performing product is the fruit (M = \$530.73, SD =\$1,247.33). The worst performing product or one that has the least total profit is the Juicing with an average of \$3.00, followed by Spices (M = \$8.39, SD =\$16.33).

Are the different locations in the shop having the same sales? The Analysis of Variance (ANOVA) was carried to test whether the net sales were statistically different on different shop location. The summary is as tabulated below.

 ANOVA Table Sum of Squares df Mean Square F Sig. Total Sales (\$) * Location of product in shop Between Groups (Combined) 134299725.024 4 33574931.256 37.176 .000 Within Groups 929333380.817 1029 903142.255 Total 1063633105.841 1033

The evidence shows that the claim that the average sale of different location is not different should be rejected (F = 4, 1029) = 37.176, p-value < .05). In short, we can claim that at least one of the location average sales total is significantly different. A 95% error bar (confidence interval) was plotted to assess which location had different average sales. In accordance with Keller, (2014) this has the same results to that of post hoc analysis.

## Statistics

The error bar (95% confidence interval) of the Front, outside front, and rear overlap, indicating that their sales are not significantly different (Farnsworth, 2016). The right and left location confidence interval do not overlap with the other location error bar, implying their average is significantly different.

It is important to determine whether the profit earned from different shop location differs. This will help determine whether the difference in the sales significantly influences the profit earned.

 ANOVA Table Sum of Squares df Mean Square F Sig. Net Profit (\$) * Location of product in shop Between Groups (Combined) 36561758.739 4 9140439.685 46.211 .000 Within Groups 203534122.514 1029 197797.981 Total 240095881.253 1033

The at least one locations net profit is different (F (4, 1029) = 46.211 p < .05). The error bar is as displayed below.

The net profit for the outside front and front are not statistically different. The left and right makes the least profit. This implies that the location of the shop determines the amount of profit earned.

An assessment was carried out to determine whether the average sales and profit of the enterprise differ in different months of the year. The claim, in this case, is that the sale and profit are equal throughout the year. The analysis is as illustrated in the ANOVA table below.

 ANOVA Table Sum of Squares df Mean Square F Sig. Average_Sale * Month of the year Between Groups (Combined) 335.651 11 30.514 1.979 .030 Within Groups 5333.831 346 15.416 Total 5669.483 357
 ANOVA Table Sum of Squares df Mean Square F Sig. Profit Total * Month of the year Between Groups (Combined) 35370.948 11 3215.541 3.867 .000 Within Groups 294370.006 354 831.554 Total 329740.954 365

At least one of the average sale and profits is significantly different in different months of the year since both p-values < .05. Therefore, there is a need to determine which month is not performing consistently with the other months. An error bar was portrayed to determine which months were not consistent.

The chart shows that the average of the gross sales for June is significantly different from that of November since their 95% confidence intervals do not overlap (Farnsworth, 2016).

The average profit total for April and June are significantly different from those yields in August, September, and October. It should be noted that although the gross sale in April is high, the total profit obtained is low.

It is important to assess whether the average gross sale was significantly different in the four seasons. The test was conducted, and the results were as follows.

 ANOVA Table Sum of Squares df Mean Square F Sig. Gross_Sales * Season of the year Between Groups (Combined) 560240.410 3 186746.803 1.765 .153 Within Groups 38298267.520 362 105796.319 Total 38858507.929 365

Evidence points that all the seasons average gross sales were not significantly different (F (3, 362) = 1.765, p-value = 0.153) (Keller, 2014). This implies that the seasons of the year do not affect the gross sale of the shop. In particular, the average gross sales are still the same in different seasons of the year.

The confidence interval of all the seasons overlaps, supporting the evidence that the average gross sales in different seasons are not significantly different (Farnsworth, 2016).

However, the assessment of whether the average profit total differs in the four seasons show otherwise.

 ANOVA Table Sum of Squares df Mean Square F Sig. Profit Total * Season of the year Between Groups (Combined) 28591.757 3 9530.586 11.456 .000 Within Groups 301149.197 362 831.904 Total 329740.954 365

The finding is that at least one of the season has a different average profit total (F (3, 362) = 11.456, p-value < .05)

The chart shows that the Autumn has the least average profit totals different from the rest of the four seasons. The average profit for the summer and spring were not significantly different, but Winter had a significantly different average to that of spring.

A regression model was fitted to determine whether there is an association between rainfall and the profit total the organization makes. The summary of the model is as follows.

 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .008a .000 -.003 30.13165 a. Predictors: (Constant), Rainfall b. Dependent Variable: Profit Total
 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 19.078 1 19.078 .021 .885b Residual 329573.612 363 907.916 Total 329592.689 364 a. Dependent Variable: Profit Total b. Predictors: (Constant), Rainfall
 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 30.650 1.702 18.007 .000 Rainfall .023 .161 .008 .145 .885 a. Dependent Variable: Profit Total

The model is not significant to be used to predict the profit total earned by the shop using the rainfall (F (1, 363) = 0.021, p-value = 0.885) (Keller, 2014). The findings indicate that there is no significant association between the predictor (rainfall) and the dependent variable (profit total).

It was important to determine whether the gross sales were associated with the rainfall. The analysis results are as summarized below.

 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .033a .001 -.002 326.981 a. Predictors: (Constant), Rainfall b. Dependent Variable: Gross_Sales
 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 43060.065 1 43060.065 .403 .526b Residual 38810647.758 363 106916.385 Total 38853707.823 364 a. Dependent Variable: Gross_Sales b. Predictors: (Constant), Rainfall

The model is also not significant (F (1, 363) = 0.403, p-value > .05) (Keller, 2014). This indicates that gross sales and rainfall are not related.

Conclusion

The results of the research pointed that:

1. The location of the shop determines the amount of profit earned.
2. The location average sales total is significantly different
3. The net profit is different in different shop locations. The net profit for the outside front and front are not statistically different. The left and right make the least profit.
4. The average sale and profits are significantly different in different months of the year.
5. All the season's average gross sales were not significantly different.
6. The autumn has the least average profit totals different from the rest of the four seasons. The average profit for the summer and spring were not significantly different, but Winter had a significantly different average to that of spring
7. There is no significant association between the predictor (rainfall) and the dependent variable (profit total)
8. Gross sales and rainfall are not related

The business manager should identify important factors which makes the total profit fluctuate on different months and seasons. For, instance, are there other hidden costs that increase during different months or seasons. This is important season the gross sales remain the same in different months, but the profit is not consistent. The business should also note that different shop location yields different returns, which means that they should maximize the profit of these locations. This can be achieved through stocking fast-moving products more at those locations. Further, the company should try to improve the performance of commodities like juicing and spices.

References

Farnsworth, D. L. (2016). Confidence interval instead of hypothesis tests. Mathematics and Computer Education, 50(2), 130.

Keller, G. (2014). Statistics for management and economics. Nelson Education.

Rietveld, T., & Hout, R. v. (2017). The paired t test and beyond: Recommendations for testing the central tendencies of two paired samples in research on speech, language and hearing pathology. Journal of Communication Disorders, 69, 44-57.

Cite This Work

"Business Performance Analysis: Sales, Profit, And Product Location." My Assignment Help, 2021, https://myassignmenthelp.com/free-samples/bus501-business-analytics-and-statistics/marking-criteria.html.

My Assignment Help (2021) Business Performance Analysis: Sales, Profit, And Product Location [Online]. Available from: https://myassignmenthelp.com/free-samples/bus501-business-analytics-and-statistics/marking-criteria.html
[Accessed 29 May 2024].

My Assignment Help. 'Business Performance Analysis: Sales, Profit, And Product Location' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/bus501-business-analytics-and-statistics/marking-criteria.html> accessed 29 May 2024.

My Assignment Help. Business Performance Analysis: Sales, Profit, And Product Location [Internet]. My Assignment Help. 2021 [cited 29 May 2024]. Available from: https://myassignmenthelp.com/free-samples/bus501-business-analytics-and-statistics/marking-criteria.html.

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